I am trying to apply a rollapply mean function to a dataframe with large chunks of missing data and single points interspersed throughout the missing data. Using my current form of rollapply, only one non-NaN value is needed and averaged to all surrounding values. My goal is to keep the values that have at least half of the values averaged within them, and remove all values that are the result of over 50% NaN data. Below is a simple example I made
library(zoo)
a <- c(0:20)
ind <- c(10:15)
ind2 <- c(10:12,14)
b <- a
b[ind] <- NaN
c <- a
c[ind2] <- NaN
df <- data.frame(a=a,b=b,c=c)
dfroll <- as.data.frame(rollapply(df,width=5,mean,na.rm=T,fill=NA))
This results in the following data.frame
dfroll
a b c
1 NA NA NA
2 NA NA NA
3 2 2.0 2.00000
4 3 3.0 3.00000
5 4 4.0 4.00000
6 5 5.0 5.00000
7 6 6.0 6.00000
8 7 6.5 6.50000
9 8 7.0 7.00000
10 9 7.5 7.50000
11 10 8.0 10.00000
12 11 NaN 12.00000
13 12 NaN 13.00000
14 13 15.0 13.66667
15 14 15.5 14.25000
16 15 16.0 15.50000
17 16 16.5 16.00000
18 17 17.0 17.00000
19 18 18.0 18.00000
20 NA NA NA
21 NA NA NA
For this example, I would like all values that have >= 3 NaN values to result in NaN. This would be rows 10:15 on column b, and rows 10:13 on column c. How would I go about setting up this threshold to pass NaN rather than the averaged value if there is limited data around the cell?
Desired result:
bdesired <- dfroll$b
bdesired[c(10:15)] <- NaN
cdesired <- dfroll$c
cdesired[c(10:13)] <- NaN
dfdesired <- data.frame(a=dfroll$a,b=bdesired,c=cdesired)
dfdesired
a b c
1 NA NA NA
2 NA NA NA
3 2 2.0 2.00000
4 3 3.0 3.00000
5 4 4.0 4.00000
6 5 5.0 5.00000
7 6 6.0 6.00000
8 7 6.5 6.50000
9 8 7.0 7.00000
10 9 NaN NaN
11 10 NaN NaN
12 11 NaN NaN
13 12 NaN NaN
14 13 NaN 13.50000
15 14 NaN 14.33333
16 15 16.0 16.00000
17 16 16.5 16.50000
18 17 17.0 17.00000
19 18 18.0 18.00000
20 NA NA NA
21 NA NA NA
1) Define a function which returns NaN if there are thresh or more NA's in its input and returns mean of the non-NA's otherwise. Then use it with rollapply. Convert that to a data frame if desired using as.data.frame but since the data is entirely numeric leaving it as a matrix may be sufficient.
w <- 5
thresh <- w/2
Mean <- function(x, thresh) if (sum(is.na(x)) > thresh) NaN else mean(x,na.rm=TRUE)
rollapply(df, w, Mean, thresh = thresh, fill = NA)
2) Another possibility is to check if there are more than thresh NA's in each cell and if so return NaN and otherwise return the rolling mean. Again use as.data.frame on the result if a data frame is needed. (1) has the advantage over this one that it only calls roll* once instead of twice.
w <- 5
thresh <- w/2
ifelse(rollsum(is.na(df), w, fill = NA) > thresh, NaN,
rollmean(df, w, na.rm = TRUE, fill = NA))